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Automated Detection and Diagnosis of Diabetic Retinopathy: a Comprehensive Survey

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Automated Detection and Diagnosis of Diabetic Retinopathy: a Comprehensive Survey Journal of Imaging Review Automated Detection and Diagnosis of Diabetic Retinopathy: A Comprehensive Survey Vasudevan Lakshminarayanan 1,* , Hoda Kheradfallah 1, Arya Sarkar 2 and Janarthanam Jothi Balaji 3 1 Theoretical and Experimental Epistemology Lab, School of Optometry and Vision Science, University of Waterloo, Waterloo, ON N2L 3G1, Canada; [email protected] 2 Department of Computer Engineering, University of Engineering and Management, Kolkata 700 156, India; [email protected] 3 Department of Optometry, Medical Research Foundation, Chennai 600 006, India; [email protected] * Correspondence: [email protected] Abstract: Diabetic Retinopathy (DR) is a leading cause of vision loss in the world. In the past few years, artificial intelligence (AI) based approaches have been used to detect and grade DR. Early detection enables appropriate treatment and thus prevents vision loss. For this purpose, both fundus and optical coherence tomography (OCT) images are used to image the retina. Next, Deep-learning (DL)-/machine-learning (ML)-based approaches make it possible to extract features from the images and to detect the presence of DR, grade its severity and segment associated lesions. This review covers the literature dealing with AI approaches to DR such as ML and DL in classification and segmentation that have been published in the open literature within six years (2016–2021). In addition, a comprehensive list of available DR datasets is reported. This list was constructed using both the PICO (P-Patient, I-Intervention, C-Control, O-Outcome) and Preferred Reporting Items for Systematic Review and Meta-analysis (PRISMA) 2009 search strategies. We summarize a total of 114 Citation: Lakshminarayanan, V.; published articles which conformed to the scope of the review. In addition, a list of 43 major datasets Kheradfallah, H.; Sarkar, A.; Balaji, J.J. is presented. Automated Detection and Diagnosis of Diabetic Retinopathy: A Keywords: diabetic retinopathy; artificial intelligence; deep learning; machine-learning; datasets; Comprehensive Survey. J. Imaging fundus image; optical coherence tomography; ophthalmology 2021, 7, 165. https://doi.org/ 10.3390/jimaging7090165 Academic Editor: Reyer Zwiggelaar 1. Introduction Diabetic retinopathy (DR) is a major cause of irreversible visual impairment and Received: 30 June 2021 blindness worldwide [1]. This etiology of DR is due to chronic high blood glucose levels, Accepted: 24 August 2021 Published: 27 August 2021 which cause retinal capillary damage, and mainly affects the working-age population. DR begins at a mild level with no apparent visual symptoms but it can progress to severe Publisher’s Note: MDPI stays neutral and proliferated levels and progression of the disease can lead to blindness. Thus, early with regard to jurisdictional claims in diagnosis and regular screening can decrease the risk of visual loss to 57.0% as well as published maps and institutional affil- decreasing the cost of treatment [2]. iations. DR is clinically diagnosed through observation of the retinal fundus either directly or through imaging techniques such as fundus photography or optical coherence tomography. There are several standard DR grading systems such as the Early Treatment Diabetic Retinopathy Study (ETDRS) [3]. ETDRS separates fine detailed DR characteristics using multiple levels. This type of grading is done upon all seven retinal fundus Fields of View Copyright: © 2021 by the authors. (FOV). Although ETDRS [4] is the gold standard, due to implementation complexity and Licensee MDPI, Basel, Switzerland. This article is an open access article technical limitations [5], alternative grading systems are also used such as the International distributed under the terms and Clinical Diabetic Retinopathy (ICDR) [6] scale which is accepted in both clinical and conditions of the Creative Commons Computer-Aided Diagnosis (CAD) settings [7]. The ICDR scale defines 5 severity levels Attribution (CC BY) license (https:// and 4 levels for Diabetic Macular Edema (DME) and requires fewer FOVs [6]. The ICDR creativecommons.org/licenses/by/ levels are discussed below and are illustrated in Figure1. 4.0/). J. Imaging 2021, 7, 165. https://doi.org/10.3390/jimaging7090165 https://www.mdpi.com/journal/jimaging J. Imaging 2021, 7, 165 2 of 26 Figure 1. Retinal fundus images of different stages of diabetic retinopathy. (A) Stage II: Mild non-proliferative dia- betic retinopathy; (B) Stage III: Moderate non-proliferative diabetic retinopathy; (C) Stage IV: Severe non-proliferative diabetic retinopathy; (D) Stage V: Proliferative diabetic retinopathy (images courtesy of Rajiv Raman et al., Sankara Nethralaya, India). • No Apparent Retinopathy: No abnormalities. • Mild Non-Proliferative Diabetic Retinopathy (NPDR): This is the first stage of diabetic retinopathy, specifically characterized by tiny areas of swelling in retinal blood vessels known as Microaneurysms (MA) [8]. There is an absence of profuse bleeding in retinal nerves and if DR is detected at this stage, it can help save the patient’s eyesight with proper medical treatment (Figure1A). • Moderate NPDR: When left unchecked, mild NPDR progresses to a moderate stage when there is blood leakage from the blocked retinal vessels. Additionally, at this stage, Hard Exudates (Ex) may exist (Figure1B). Furthermore, the dilation and constriction of venules in the retina causes Venous Beadings (VB) which are visible ophthalmospi- cally [8]. • Severe NPDR: A larger number of retinal blood vessels are blocked in this stage, causing over 20 Intra-retinal Hemorrhages (IHE; Figure1C) in all 4 fundus quadrants or there are Intra-Retinal Microvascular Abnormalities (IRMA) which can be seen as bulges of thin vessels. IRMA appears as small and sharp-border red spots in at least one quadran. Furthermore, there can be a definite evidence of VB in over 2 quadrants [8]. • Proliferative Diabetic Retinopathy (PDR): This is an advanced stage of the disease that occurs when the condition is left unchecked for an extended period of time. New J. Imaging 2021, 7, 165 3 of 26 blood vessels form in the retina and the condition is termed Neovascularization (NV). These blood vessels are often fragile, with a consequent risk of fluid leakage and proliferation of fibrous tissue [8]. Different functional visual problems occur at PDR, such as blurriness, reduced field of vision, and even complete blindness in some cases (Figure1D). DR detection has two main steps: screening and diagnosis. For this purpose, fine pathognomonic DR signs in initial stages are determined normally, after dilating pupils (mydriasis). Then, DR screening is performed through slit lamp bio-microscopy with a + 90.0 D lens, and direct [9]/indirect ophthalmoscopy [10]. The next step is to diagnose DR which is done through finding DR-associated lesions and comparing with the standard grading system criteria. Currently, the diagnosis step is done manually. This procedure is costly, time consuming and requires highly trained clinicians who have considerable experience and diagnostic precision. Even if all these resources are available there is still the possibility of misdiagnosis [11]. This dependency on manual evaluation makes the situation challenging. In addition, in year 2020, the number of adults worldwide with DR, and vision-threatening DR was estimated to be 103.12 million, and 28.54 million. By the year 2045, the numbers are projected to increase to 160.50 million, and 44.82 million [12]. In addition, in developing countries where there is a shortage of ophthalmologists [13,14] as well as access to standard clinical facilities. This problem also exists in underserved areas of the developed world. Recent developments in CAD techniques, which are defined in the subscope of artifi- cial intelligence (AI), are becoming more prominent in modern ophthalmology [15] as they can save time, cost and human resources for routine DR screening and involve lower diag- nostic error factors [15]. CAD can also efficiently manage the increasing number of afflicted DR patients [16] and diagnose DR in early stages when fewer sight threatening effects are present. The scope of AI based approaches are divided between Machine Learning-based (ML) and Deep Learning-based (DL) solutions. These techniques vary depending on the imaging system and disease severity. For instance, in early levels of DR, super-resolution ultrasound imaging of microvessels [17] is used to visualize the deep ocular vasculature. On this imaging system, a successful CAD method applied a DL model for segmenting lesions on ultrasound images [18]. The widely applied imaging methods such as Optical Coherence Tomography (OCT), OCT Angiography (OCTA), Ultrawide-field fundus (UWF) and standard 45◦ fundus photography are covered in this review. In addition to the men- tioned imaging methods, Majumder et al. [15] reported a real time DR screening procedure using a smartphone camera. The main purpose of this review is to analyze 114 articles published within the last 6 years that focus on the detection of DR using CAD techniques. These techniques have made considerable progress in performance with the use of ML and DL schemes that employ the latest developments in Deep Convolutional Neural Networks (DCNNs) architectures for DR severity grading, progression analysis, abnormality detection and semantic segmentation. An overview of ophthalmic applications of convolutional neural networks is presented in [19–22]. 2. Methods 2.1. Literature Search Details For this review, literature
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